AI0-001 AI Security, Ethics and Governance Practice Question
A healthcare organization uses an AI model to predict patient readmission risk. To comply with patient privacy regulations, they apply differential privacy during training. What is the primary trade-off of using differential privacy?
⚠ Common exam trap
The AI0-001 exam often tests the misconception that differential privacy primarily reduces bias or improves fairness, when in fact its core trade-off is accuracy for privacy, and fairness can be negatively impacted by the added noise.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduced model accuracy for increased privacy
Differential privacy works by adding calibrated noise to the training process or model outputs, which directly reduces the model's accuracy in exchange for a quantifiable privacy guarantee (e.g., ε-differential privacy). This trade-off is fundamental: stronger privacy (lower ε) requires more noise, which degrades predictive performance. The healthcare organization must balance the need to protect patient data against the clinical utility of accurate readmission predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increased training time for reduced bias
Why it's wrong here
Differential privacy adds calibrated noise, trading model accuracy and utility for privacy guarantees; it does not primarily extend training time or target bias. It would be chosen when formal, quantifiable privacy protection of individuals in training data is the compliance requirement, accepting some predictive degradation.
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Lower interpretability for higher fairness
Why it's wrong here
Differential privacy's guarantee concerns individual privacy, not fairness or interpretability; noise injection does not systematically reduce explainability. It is selected when a mathematical privacy bound on training data is mandated. Fairness and interpretability are addressed by separate techniques such as bias auditing and explainability methods.
- ✗
Faster inference for lower memory usage
Why it's wrong here
Differential privacy adds calibrated noise to training data or gradients, degrading model accuracy and slowing training; it does not affect inference speed or memory. The option is tempting because privacy-preserving techniques are often assumed to reduce resource overhead, but inference optimisation is achieved through quantisation or pruning, not differential privacy.
- ✓
Reduced model accuracy for increased privacy
Why this is correct
Differential privacy injects calibrated noise into training data or gradients, mathematically bounding any individual's influence on the model. This privacy guarantee inherently perturbs learned parameters, degrading predictive performance. The trade-off is therefore measurable accuracy loss, which the healthcare scenario accepts to satisfy patient privacy regulations.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.